2019_analysis

R setup

# Load libraries
library(plyr)
library(tidyverse)
library(edgeR)
library(AnnotationHub)
library(magrittr)
library(scales)
library(pander)
library(ggrepel)
library(fgsea)
library(pheatmap)
library(igraph)
library(tidygraph)
library(ggraph)
library(grid)
library(RUVSeq)

# Set working directory
setwd("~/Documents/Bioinformatics/Projects/20190122_Q96K97_NoStress_RNASeq/R")

2019 data analysis

Differential Expression Analysis

Load data

# Load counts analysed by feature counts
counts <- read_tsv("../2_alignedData/featureCounts/genes.out") %>%
  set_colnames(basename(colnames(.))) %>%
  set_colnames(str_remove(colnames(.), "Aligned.sortedByCoord.out.bam")) %>%
  dplyr::select(Geneid, starts_with("W"), starts_with("Q"))

Create DGEList

# Create DGEList and calculate normalisaton factors
dgeList <- counts %>%
  as.data.frame() %>%
  column_to_rownames("Geneid") %>%
  DGEList() %>%
  calcNormFactors()

# Set group variable
dgeList$samples$group <- colnames(dgeList) %>%
  str_extract("(W|Q)") %>%
  factor(levels = c("W", "Q"))

Add gene information

# Add AnnotationHub and subset to search for zebrafish
ah <- AnnotationHub()
ah %>%
  subset(species == "Danio rerio") %>%
  subset(dataprovider == "Ensembl") %>%
  subset(rdataclass == "EnsDb")

## AnnotationHub with 11 records
## # snapshotDate(): 2019-05-02 
## # $dataprovider: Ensembl
## # $species: Danio rerio
## # $rdataclass: EnsDb
## # additional mcols(): taxonomyid, genome, description,
## #   coordinate_1_based, maintainer, rdatadateadded, preparerclass,
## #   tags, rdatapath, sourceurl, sourcetype 
## # retrieve records with, e.g., 'object[["AH53201"]]' 
## 
##             title                           
##   AH53201 | Ensembl 87 EnsDb for Danio Rerio
##   AH53705 | Ensembl 88 EnsDb for Danio Rerio
##   AH56671 | Ensembl 89 EnsDb for Danio Rerio
##   AH57746 | Ensembl 90 EnsDb for Danio Rerio
##   AH60762 | Ensembl 91 EnsDb for Danio Rerio
##   ...       ...                             
##   AH64434 | Ensembl 93 EnsDb for Danio Rerio
##   AH64906 | Ensembl 94 EnsDb for Danio rerio
##   AH67932 | Ensembl 95 EnsDb for Danio rerio
##   AH69169 | Ensembl 96 EnsDb for Danio rerio
##   AH73861 | Ensembl 97 EnsDb for Danio rerio

# Select correct Ensembl release
ensDb <- ah[["AH64906"]]

# Extract GenomicRanges object from ensDb
genesGR <- genes(ensDb)

# Remove redundant columns from mcols
mcols(genesGR) <- mcols(genesGR)[c("gene_id", "gene_name", 
                                   "gene_biotype", "entrezid")]

transGR <- transcripts(ensDb)
mcols(transGR) <- mcols(transGR)[c("tx_id", "gene_id")]
trans2Gene <- tibble(
  tx_id = transGR$tx_id,
  gene_id = transGR$gene_id
)

# Add genesGR to DGEList using rownames of DGEList to reorder the genesGR
dgeList$genes <- genesGR[rownames(dgeList),]

Data QC

# Perform logical test to see how many genes were not detected in dataset
dgeList$counts %>%
  rowSums() %>%
  is_greater_than(0) %>%
  table()

## .
## FALSE  TRUE 
##  3927 28130

# Check for genes having > 4 samples with cpm > 1
dgeList %>%
  cpm() %>%
  is_greater_than(1) %>%
  rowSums() %>%
  is_weakly_greater_than(4) %>%
  table()

## .
## FALSE  TRUE 
## 13704 18353

# Create logical vector of genes to keep that fit criteria
genes2keep <- dgeList %>%
  cpm() %>%
  is_greater_than(1) %>%
  rowSums() %>%
  is_weakly_greater_than(4)

# Create new DGEList of genes fitting criteria
dgeFilt <- dgeList[genes2keep,, keep.lib.sizes = FALSE] %>%
  calcNormFactors()

# Compare distributions of the DGELists before and after filtering
par(mfrow = c(1,2))
dgeList %>%
  cpm(log = TRUE) %>%
  plotDensities(legend = FALSE, main = "Before Filtering")
dgeFilt %>%
  cpm(log = TRUE) %>%
  plotDensities(legend = FALSE, main = "After Filtering")

par(mfrow = c(1,1))

Library sizes

# Check library sizes with box plot
dgeFilt$samples %>%
  ggplot(aes(group, lib.size, fill = group)) +
  geom_boxplot() +
  scale_y_continuous(labels = comma) +
  labs(x = "Genotype", y = "Library Size") +
  scale_fill_discrete(
    name ="Genotype", 
    labels = c("Wildtype", "Mutant")
  ) +
  scale_x_discrete(labels=c("W" = "Wildtype", "Q" = "Mutant")) +
  theme_bw()

PCA

# Assess cpm values to make sure PCA results are not heavily skewed by highly expressed genes
pca <- dgeFilt %>%
  cpm(log = TRUE) %>%
  t() %>%
  prcomp()

# Quick inspection to check whether first two PCA components capture most of the variability
summary(pca)$importance %>% pander(split.tables = Inf)

	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8	PC9
Standard deviation	22.27	18.07	16.75	14.73	14.45	13.34	11.87	11.2	5.671e-14
Proportion of Variance	0.2513	0.1655	0.1421	0.1099	0.1058	0.09023	0.07145	0.06362	0
Cumulative Proportion	0.2513	0.4168	0.559	0.6689	0.7747	0.8649	0.9364	1	1

# Plot PCA
pca$x %>%
  as.data.frame() %>%
  rownames_to_column("sample") %>%
  as_tibble() %>%
  dplyr::select(sample, PC1, PC2) %>%
  left_join(rownames_to_column(dgeFilt$samples, "sample")) %>%
  ggplot(aes(PC1, PC2, colour = group, label = sample)) +
  geom_point() +
  geom_text_repel(show.legend = FALSE) +
  theme_bw()

Differential expression

# Create model matrix
design <- model.matrix(~group, data = dgeFilt$samples)

# Perform exact test on DGEList
topTable <- dgeFilt %>%
  estimateDisp(design = design) %>%
  exactTest() %>%
  topTags(n = Inf) %>%
  .$table %>%
  as_tibble() %>%
  unite("Range", ID.start, ID.end, sep = "-") %>%
  unite("Location", ID.seqnames, Range, ID.strand, sep = ":") %>%
  dplyr::select(
    Geneid = ID.gene_id, 
    Symbol = ID.gene_name,
    AveExpr = logCPM, logFC, 
    P.Value = PValue, 
    FDR, Location, 
    Entrez = ID.entrezid
  ) %>%
  mutate(DE = FDR < 0.05)

# Volcano plot showing DE genes
topTable %>%
  ggplot(aes(logFC, -log10(P.Value), colour = DE)) +
  geom_point(alpha = 0.5) +
  geom_text_repel(data = . %>% 
                    dplyr::filter(DE) %>%
                    dplyr::filter(-log10(P.Value) > 4 | abs(logFC) >                        2.5), aes(label = Symbol)) + 
  scale_colour_manual(values = c("grey", "red")) +
  theme_bw() +
  theme(legend.position = "none")

# MD Plot showing DE genes
topTable %>%
  dplyr::arrange(desc(P.Value)) %>%
  ggplot(aes(AveExpr, logFC, colour = DE)) +
  geom_point(alpha = 0.5) +
  geom_text_repel(
    data = . %>% 
      dplyr::filter(DE) %>%
      dplyr::filter(abs(logFC) > 2 | AveExpr > 14),
    aes(label = Symbol)
  ) + 
  scale_colour_manual(values = c("grey", "red")) +
  labs(x = "Average Expression (log2 CPM)",
       y = "log Fold-Change") +
  theme_bw() +
  theme(legend.position = "none")

# Summary of DE genes
topTableDE <- topTable %>%
  dplyr::filter(FDR < 0.05) %>%
  dplyr::select(Geneid, Symbol, AveExpr, logFC, P.Value, FDR) 
topTableDE %>% pander(style = "rmarkdown", split.tables = Inf)

Geneid	Symbol	AveExpr	logFC	P.Value	FDR
ENSDARG00000091368	AL954327.1	2.656	-5.923	1.214e-08	0.0002228
ENSDARG00000093214	si:ch211-284e13.9	0.8189	1.542	6.086e-07	0.005585
ENSDARG00000037421	egr1	8.527	-0.712	9.887e-07	0.006049
ENSDARG00000017246	prx	2.326	-2.614	1.823e-06	0.008364
ENSDARG00000089477	si:ch211-132g1.3	5.855	0.6079	3.135e-06	0.01089
ENSDARG00000089382	zgc:158463	5.631	0.6536	3.561e-06	0.01089
ENSDARG00000080337	NC_002333.4	11.17	0.4449	5.344e-06	0.01401
ENSDARG00000096829	blvrb	3.025	-1.521	1.643e-05	0.03386
ENSDARG00000093438	CU467110.1	4.596	0.5459	1.752e-05	0.03386
ENSDARG00000091916	ugt5b4	-0.0384	-1.445	1.994e-05	0.03386
ENSDARG00000036304	dnaaf3l	1.358	-1.44	2.029e-05	0.03386

GO Enrichment

ens2Entrez <- file.path("https://uofabioinformaticshub.github.io/Intro-NGS-fib",
                        "data", "ens2Entrez.tsv") %>% 
  url() %>%
  read_tsv()

de <- topTable %>%
  dplyr::filter(FDR < 0.05) %>%
  dplyr::select(Geneid) %>%
  left_join(ens2Entrez) %>%
  dplyr::filter(!is.na(Entrez)) %>%
  .[["Entrez"]] %>%
  unique()
uv <- topTable %>%
  dplyr::select(Geneid) %>%
  left_join(ens2Entrez) %>%
  dplyr::filter(!is.na(Entrez)) %>%
  .[["Entrez"]] %>%
  unique()

goResults <- goana(de = de, universe = uv, species = "Hs")

goResults %>% 
  rownames_to_column("GO ID") %>%
  as_tibble() %>%
  dplyr::filter(DE > 1) %>%
  dplyr::arrange(P.DE) %>%
  mutate(FDR = p.adjust(P.DE, "fdr")) %>%
  dplyr::filter(FDR < 0.05) %>%
  mutate(`GO ID` = str_replace(`GO ID`, ":", "\\\\:")) %>%
  pander(caption = "GO Terms potentially enriched in the set of differentially expressed genes")

GO Terms potentially enriched in the set of differentially expressed genes

GO ID

Term

Ont

N

DE

P.DE

FDR

Gene Set Enrichment Analysis (GSEA)

Setting up ID conversion, ranks and pathways

# Load id conversion file
idConvert <- read_csv2("../files/zf2human_withEntrezIDs.csv") %>%
  dplyr::select(Geneid = zfID, EntrezID = Entrez) %>%
  mutate(EntrezID = as.character(EntrezID))
# Create function to convert ids (Not sure how this works, Steve wrote it)
convertHsEG2Dr <- function(ids, df = idConvert){
  dplyr::filter(df, EntrezID %in% ids)$Geneid
}
# Conversion of zebrafish ensembl ID to zebrafish symbol, for plotting on network analyses
idConvertSymbol <- read_csv2("../files/zf2human_withEntrezIDs.csv") %>%
  dplyr::select(label = zfID, symbol = zfName) %>%
  na.omit() %>%
  unique()

# Create named vector of gene level statistics 
ranks <- topTable %>%
  mutate(stat = -sign(logFC) * log10(P.Value)) %>%
  dplyr::arrange(desc(stat)) %>%
  with(structure(stat, names = Geneid))

# Import hallmark human gene genesets and tidy gene set names
# .gmt files downloaded from:
# http://software.broadinstitute.org/gsea/downloads.jsp 
# http://data.wikipathways.org/20190610/ 
hallmark <- gmtPathways("../files/h.all.v6.2.entrez.gmt") %>%
  mclapply(convertHsEG2Dr, mc.cores = 4) %>%
  set_names(str_remove_all(names(.), "HALLMARK_"))
kegg <- gmtPathways("../files/c2.cp.kegg.v6.2.entrez.gmt") %>%
  mclapply(convertHsEG2Dr, mc.cores = 4) %>%
  set_names(str_remove_all(names(.), "KEGG_"))
wiki <- gmtPathways("../files/wikipathways-20190610-gmt-Homo_sapiens.gmt") %>%
  mclapply(convertHsEG2Dr, mc.cores = 4) %>%
  set_names(str_remove_all(names(.), "%.+"))

Hallmark

# Set seed to enable reproducibility
set.seed(22)
# Run GSEA for hallmark
fgseaHallmark <- fgsea(hallmark, ranks, nperm=1e5) %>%
  as_tibble() %>%
  dplyr::rename(FDR = padj) %>%
  mutate(padj = p.adjust(pval, "bonferroni")) %>%
  dplyr::arrange(pval)
# Create an object of pathways with adjusted p-value < 0.05 for construction of network diagrams. This should be done differently next time, but too much work has been done to change it now.
fgseaHallmarkTop <- fgseaHallmark %>%
  dplyr::filter(padj < 0.05)
fgseaHallmarkTop %>%
  dplyr::select(-leadingEdge, -nMoreExtreme) %>%
  pander(
    style = "rmarkdown", 
    split.tables = Inf, 
    justify = "lrrrrrr", 
    caption = paste(
      "The", nrow(.), "most significantly enriched Hallmark pathways.",
      "This corresponds to an FDR of", percent(max(.$FDR)))
  )

The 13 most significantly enriched Hallmark pathways. This corresponds to an FDR of 0.284%

pathway	pval	FDR	ES	NES	size	padj
MYC_TARGETS_V1	1.236e-05	0.0001011	0.6076	2.418	220	0.0006178
OXIDATIVE_PHOSPHORYLATION	1.237e-05	0.0001011	0.6488	2.58	219	0.0006184
INTERFERON_GAMMA_RESPONSE	1.251e-05	0.0001011	0.5684	2.242	200	0.0006256
E2F_TARGETS	1.254e-05	0.0001011	0.464	1.827	197	0.0006268
ALLOGRAFT_REJECTION	1.256e-05	0.0001011	0.5472	2.153	195	0.0006278
DNA_REPAIR	1.304e-05	0.0001011	0.5417	2.069	150	0.0006519
INTERFERON_ALPHA_RESPONSE	1.415e-05	0.0001011	0.5773	2.034	83	0.0007075
MYOGENESIS	5.222e-05	0.0003264	-0.4095	-1.859	216	0.002611
WNT_BETA_CATENIN_SIGNALING	6.038e-05	0.0003354	-0.5614	-2.034	52	0.003019
ADIPOGENESIS	0.0001853	0.0008935	0.4063	1.617	220	0.009267
MTORC1_SIGNALING	0.0001966	0.0008935	0.4066	1.624	228	0.009829
G2M_CHECKPOINT	0.0005689	0.002371	0.3955	1.572	216	0.02845
KRAS_SIGNALING_DN	0.0007387	0.002841	-0.3669	-1.599	155	0.03694

# Make a table plot of significant Hallmark pathways
if (interactive()) grid::grid.newpage()
plotGseaTable(
  hallmark[fgseaHallmark$pathway], ranks, fgseaHallmark, gseaParam = 0.5
)

KEGG

# Set seed to enable reproducibility
set.seed(22)
# Run GSEA for KEGG
fgseaKEGG <- fgsea(kegg, ranks, nperm=1e5) %>%
  as_tibble() %>%
  dplyr::rename(FDR = padj) %>%
  mutate(padj = p.adjust(pval, "bonferroni")) %>%
  dplyr::arrange(pval)
# Create an object of pathways with adjusted p-value < 0.05 for construction of network diagrams. This should be done differently next time, but too much work has been done to change it now.
fgseaKEGGTop <- fgseaKEGG %>%
  dplyr::filter(padj < 0.05)
fgseaKEGGTop %>%
  dplyr::select(-leadingEdge, -nMoreExtreme) %>%
  pander(
    style = "rmarkdown", 
    split.tables = Inf, 
    justify = "lrrrrrr", 
    caption = paste(
      "The", nrow(.), "most significantly enriched KEGG pathways.",
      "This corresponds to an FDR of", percent(max(.$FDR)))
  )

The 29 most significantly enriched KEGG pathways. This corresponds to an FDR of 0.171%

pathway	pval	FDR	ES	NES	size	padj
CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION	1.207e-05	0.0002836	0.4479	1.812	260	0.002246
CHEMOKINE_SIGNALING_PATHWAY	1.268e-05	0.0002836	0.6442	2.524	187	0.002358
HUNTINGTONS_DISEASE	1.276e-05	0.0002836	0.5849	2.281	180	0.002373
ALZHEIMERS_DISEASE	1.293e-05	0.0002836	0.555	2.141	164	0.002406
SPLICEOSOME	1.333e-05	0.0002836	0.6179	2.32	131	0.002479
OXIDATIVE_PHOSPHORYLATION	1.337e-05	0.0002836	0.7172	2.685	128	0.002486
PARKINSONS_DISEASE	1.344e-05	0.0002836	0.7061	2.63	123	0.002501
RIBOSOME	1.426e-05	0.0002836	0.8662	3.032	79	0.002653
NOD_LIKE_RECEPTOR_SIGNALING_PATHWAY	1.468e-05	0.0002836	0.6316	2.126	62	0.00273
PROTEASOME	1.525e-05	0.0002836	0.7112	2.259	45	0.002836
LINOLEIC_ACID_METABOLISM	2.872e-05	0.0004856	-0.6266	-2.178	43	0.005342
ECM_RECEPTOR_INTERACTION	3.345e-05	0.0005089	-0.5644	-2.212	79	0.006222
ASCORBATE_AND_ALDARATE_METABOLISM	4.629e-05	0.0005089	-0.7435	-3.304	181	0.00861
PENTOSE_AND_GLUCURONATE_INTERCONVERSIONS	4.688e-05	0.0005089	-0.7556	-3.364	184	0.008719
STARCH_AND_SUCROSE_METABOLISM	4.85e-05	0.0005089	-0.7369	-3.307	196	0.00902
PORPHYRIN_AND_CHLOROPHYLL_METABOLISM	4.877e-05	0.0005089	-0.7057	-3.175	199	0.009071
STEROID_HORMONE_BIOSYNTHESIS	5.1e-05	0.0005089	-0.695	-3.152	213	0.009486
FOCAL_ADHESION	5.198e-05	0.0005089	-0.4533	-2.062	219	0.009669
DRUG_METABOLISM_OTHER_ENZYMES	5.222e-05	0.0005089	-0.6915	-3.146	220	0.009714
DRUG_METABOLISM_CYTOCHROME_P450	5.856e-05	0.0005089	-0.6977	-3.24	262	0.01089
RETINOL_METABOLISM	5.896e-05	0.0005089	-0.6954	-3.231	264	0.01097
METABOLISM_OF_XENOBIOTICS_BY_CYTOCHROME_P450	6.019e-05	0.0005089	-0.6907	-3.216	270	0.01119
PATHWAYS_IN_CANCER	7.027e-05	0.0005683	-0.3296	-1.568	334	0.01307
SYSTEMIC_LUPUS_ERYTHEMATOSUS	7.645e-05	0.0005925	0.61	1.93	44	0.01422
AXON_GUIDANCE	0.0001317	0.0009798	-0.3794	-1.663	162	0.0245
JAK_STAT_SIGNALING_PATHWAY	0.0001705	0.00122	-0.3899	-1.693	151	0.03172
ARRHYTHMOGENIC_RIGHT_VENTRICULAR_CARDIOMYOPATHY_ARVC	0.0002086	0.001437	-0.4541	-1.817	89	0.03881
PATHOGENIC_ESCHERICHIA_COLI_INFECTION	0.000225	0.001495	0.5804	1.894	52	0.04185
NEUROACTIVE_LIGAND_RECEPTOR_INTERACTION	0.0002659	0.001706	-0.3377	-1.544	228	0.04946

# Make a table plot of significant KEGG pathways
if (interactive()) grid::grid.newpage()
plotGseaTable(
  kegg[fgseaKEGG$pathway], ranks, fgseaKEGG, gseaParam = 0.5
)

WikiPathways

# Set seed to enable reproducibility
set.seed(22)
# Run GSEA for WikiPathways
fgseaWiki <- fgsea(wiki, ranks, nperm=1e5) %>%
  as_tibble() %>%
  dplyr::rename(FDR = padj) %>%
  mutate(padj = p.adjust(pval, "bonferroni")) %>%
  dplyr::arrange(pval)
# Create an object of pathways with adjusted p-value < 0.05 for construction of network diagrams. This should be done differently next time, but too much work has been done to change it now.
fgseaWikiTop <- fgseaWiki %>%
  dplyr::filter(padj < 0.05)
fgseaWikiTop %>%
  dplyr::select(-leadingEdge, -nMoreExtreme) %>%
  pander(
    style = "rmarkdown", 
    split.tables = Inf, 
    justify = "lrrrrrr", 
    caption = paste(
      "The", nrow(.), "most significantly enriched Wiki pathways.",
      "This corresponds to an FDR of", percent(max(.$FDR)))
  )

The 24 most significantly enriched Wiki pathways. This corresponds to an FDR of 0.181%

pathway	pval	FDR	ES	NES	size	padj
Chemokine signaling pathway	1.284e-05	0.0009715	0.5945	2.303	170	0.006691
Nonalcoholic fatty liver disease	1.299e-05	0.0009715	0.5834	2.239	156	0.006768
mRNA Processing	1.328e-05	0.0009715	0.5371	2.022	133	0.006919
Electron Transport Chain (OXPHOS system in mitochondria)	1.382e-05	0.0009715	0.7417	2.677	97	0.007198
Cytoplasmic Ribosomal Proteins	1.416e-05	0.0009715	0.8704	3.052	80	0.007377
Parkin-Ubiquitin Proteasomal System pathway	1.436e-05	0.0009715	0.6106	2.101	71	0.007483
Oxidative phosphorylation	1.466e-05	0.0009715	0.7527	2.517	60	0.007635
Mitochondrial complex I assembly model OXPHOS system	1.492e-05	0.0009715	0.7502	2.444	52	0.007772
Nicotine Metabolism	2.591e-05	0.001041	-0.768	-2.255	21	0.0135
Striated Muscle Contraction Pathway	2.788e-05	0.001041	-0.6834	-2.256	34	0.01452
Irinotecan Pathway	2.848e-05	0.001041	-0.7029	-2.396	39	0.01484
Proteasome Degradation	2.926e-05	0.001041	0.5898	1.979	61	0.01525
Estrogen metabolism	3.135e-05	0.001041	-0.645	-2.398	59	0.01633
Pregnane X Receptor pathway	3.404e-05	0.001041	-0.6214	-2.444	80	0.01773
Constitutive Androstane Receptor Pathway	3.417e-05	0.001041	-0.6225	-2.452	81	0.0178
Tamoxifen metabolism	3.485e-05	0.001041	-0.6519	-2.594	86	0.01816
Aryl Hydrocarbon Receptor Pathway	3.544e-05	0.001041	-0.6158	-2.472	91	0.01846
Codeine and Morphine Metabolism	3.598e-05	0.001041	-0.7161	-2.895	95	0.01875
TGF-beta Signaling Pathway	4.232e-05	0.00116	-0.4216	-1.822	148	0.02205
Glucuronidation	4.759e-05	0.001205	-0.7632	-3.401	187	0.02479
Focal Adhesion	5.09e-05	0.001205	-0.4598	-2.085	213	0.02652
NRF2 pathway	5.09e-05	0.001205	-0.4504	-2.043	213	0.02652
Metapathway biotransformation Phase I and II	7.519e-05	0.001703	-0.5239	-2.51	360	0.03918
Nuclear Receptors Meta-Pathway	8.361e-05	0.001815	-0.3388	-1.64	404	0.04356

# Make a table plot of significant WikiPathways pathways
if (interactive()) grid::grid.newpage()
plotGseaTable(
  wiki[fgseaWiki$pathway], ranks, fgseaWiki, gseaParam = 0.5
)

Network Analysis

Hallmark

# Load significant pathways with ONLY leading edge genes determined from GSEA analysis
sigHallmark <-
  fgseaHallmarkTop %>%
  split(f = .$pathway) %>% 
  lapply(extract2, "leadingEdge") %>% 
  lapply(unlist)

# Create a node list
pathwaysHallmark <- names(sigHallmark) %>%
  as.data.frame() %>%
  set_colnames("label") %>%
  mutate(label = as.character(label))
genesHallmark <- unique(unlist(sigHallmark)) %>% 
  as.data.frame() %>% 
  set_colnames("label") %>%
  mutate(label = as.character(label))
nodesHallmark <- full_join(pathwaysHallmark, genesHallmark, by = "label") %>%
  rowid_to_column("id")
# Then create an edge list
edgesHallmark <- ldply(sigHallmark, data.frame) %>% 
  set_colnames(c("pathway", "gene")) %>%
  mutate(gene = as.character(gene)) %>%
  left_join(nodesHallmark, by = c("pathway" = "label")) %>%
  dplyr::rename(from = id) %>%
  left_join(nodesHallmark, by = c("gene" = "label")) %>%
  dplyr::rename(to = id) %>%
  dplyr::select(from, to)

# Create tidygraph object
tidyHallmark <- 
  tbl_graph(nodes = nodesHallmark, edges = edgesHallmark, directed = FALSE) %>%
  activate(nodes) %>%
  left_join(idConvertSymbol, by = "label") %>%
  mutate(
    pathways = case_when(
      id <= nrow(fgseaHallmarkTop) ~ label
    ),
    DE = case_when(
      label %in% topTableDE$Geneid ~ symbol
    ),
    size = case_when(
      label %in% topTable$Geneid ~ 
        as.integer(row_number(label %in% topTable$Geneid)), 
      id <= nrow(fgseaHallmarkTop) ~ as.integer(4000)
    ),
    colour = case_when(
      id <= nrow(fgseaHallmarkTop) ~ rainbow(nrow(fgseaHallmarkTop))[id],
      label %in% topTableDE$Geneid ~ "black"),
    hjust = case_when(
      DE == "ugt5b4" ~ as.integer(0)),
    vjust = case_when(DE == "ugt5b4" ~ as.integer(5))
  ) %>%
  activate(edges) %>%
  mutate(
    colour = case_when(
      from <= nrow(fgseaHallmarkTop) ~ rainbow(nrow(fgseaHallmarkTop))[from]
    )
  )
# Set seed to enable reproducibility (seed selected to create graph with non-overlapping labels)
set.seed(22)
# Plot graph
ggraph(tidyHallmark, layout = "fr") +
  scale_fill_manual(
    values = c(rainbow(nrow(fgseaHallmarkTop)), "black"), 
    na.value = "gray80"
  ) +
  geom_edge_arc(
    aes(color = colour), 
    alpha = 0.5, 
    show.legend = FALSE, 
    curvature = 0.5
  ) +
  geom_node_point(
    aes(size = size, fill = colour),
    shape = 21,
    stroke = 0.5, 
    
    show.legend = FALSE
  ) +
  geom_node_label(
    aes(label = pathways),
    repel = TRUE,
    size = 3, 
    alpha = 0.7,
    label.padding = 0.1
  ) +
  geom_node_text(
    aes(label = DE, hjust = hjust, vjust = vjust), 
    repel = TRUE,
    size = 3, 
    alpha = 0.8, 
    colour = "black"
  ) +
  theme_graph() +
  theme(legend.position = "none")

KEGG

# Load significant pathways with ONLY leading edge genes determined from GSEA analysis
sigKEGG <- 
  fgseaKEGGTop %>%
  split(f = .$pathway) %>% 
  lapply(extract2, "leadingEdge") %>%
  lapply(unlist)

# Create a node list
pathwaysKEGG <- names(sigKEGG) %>%
  as.data.frame() %>%
  set_colnames("label") %>%
  mutate(label = as.character(label))
genesKEGG <- unique(unlist(sigKEGG)) %>% 
  as.data.frame() %>% 
  set_colnames("label") %>%
  mutate(label = as.character(label))
nodesKEGG <- full_join(pathwaysKEGG, genesKEGG, by = "label") %>%
  rowid_to_column("id")
# Then create an edge list
edgesKEGG <- ldply(sigKEGG, data.frame) %>% 
  set_colnames(c("pathway", "gene")) %>%
  mutate(gene = as.character(gene)) %>%
  left_join(nodesKEGG, by = c("pathway" = "label")) %>%
  dplyr::rename(from = id) %>%
  left_join(nodesKEGG, by = c("gene" = "label")) %>%
  dplyr::rename(to = id) %>%
  dplyr::select(from, to)

# Create tidygraph object
tidyKEGG <- 
  tbl_graph(nodes = nodesKEGG, edges = edgesKEGG, directed = FALSE) %>%
  activate(nodes) %>%
  left_join(idConvertSymbol, by = "label") %>%
  mutate(
    pathways = case_when(
      id <= nrow(fgseaKEGGTop) ~ label
    ),
    DE = case_when(label %in% topTableDE$Geneid ~ symbol),
    size = case_when(
      label %in% topTable$Geneid ~ row_number(label %in% topTable$Geneid), 
      id <= nrow(fgseaKEGGTop) ~ 4000L
    ) %>% as.integer(),
    colour = case_when(
      id <= nrow(fgseaKEGGTop) ~ rainbow(nrow(fgseaKEGGTop))[id],
      label %in% topTableDE$Geneid ~ "black"
    ),
    hjust = case_when(
      DE == "ugt5b4" ~ -1L,
      DE == "blvrb" ~ 7L
    ),
    vjust = case_when(
      DE == "ugt5b4" ~ 7L,
      DE == "blvrb" ~ 0L
    )
  ) %>%
  activate(edges) %>%
  mutate(
    colour = case_when(
      from <= nrow(fgseaKEGGTop) ~ rainbow(nrow(fgseaKEGGTop))[from]
    )
  )
# Set seed to enable reproducibility (seed selected to create graph with non-overlapping labels)
set.seed(26)
# Plot graph
ggraph(tidyKEGG, layout = "fr") +
  scale_fill_manual(
    values = c(rainbow(nrow(fgseaKEGGTop)), "black"), 
    na.value = "gray80"
  ) +
  geom_edge_arc(
    aes(color = colour), 
    alpha = 0.5, 
    show.legend = FALSE, 
    curvature = 0.5
  ) +
  geom_node_point(
    aes(size = size, fill = colour), 
    shape = 21, 
    stroke = 0.5, 
    show.legend = FALSE
  ) +
  geom_node_label(
    aes(label = pathways),
    repel = TRUE, 
    size = 3, 
    alpha = 0.7, 
    label.padding = 0.1
  ) +
  geom_node_text(
    aes(label = DE, hjust = hjust, vjust = vjust), 
    repel = TRUE,
    size = 3,
    alpha = 0.8, 
    colour = "black"
  ) +
  theme_graph() +
  theme(legend.position = "none")

WikiPathways

# Load significant pathways with ONLY leading edge genes determined from GSEA analysis
sigWiki <- 
  fgseaWikiTop %>%
  split(f = .$pathway) %>% 
  lapply(extract2, "leadingEdge") %>% 
  lapply(unlist)

# Create a node list
pathwaysWiki <- names(sigWiki) %>%
  as.data.frame() %>%
  set_colnames("label") %>%
  mutate(label = as.character(label))
genesWiki <- unique(unlist(sigWiki)) %>% 
  as.data.frame() %>% 
  set_colnames("label") %>%
  mutate(label = as.character(label))
nodesWiki <- full_join(pathwaysWiki, genesWiki, by = "label") %>%
  rowid_to_column("id")
# Then create an edge list
edgesWiki <- ldply(sigWiki, data.frame) %>% 
  set_colnames(c("pathway", "gene")) %>%
  mutate(gene = as.character(gene)) %>%
  left_join(nodesWiki, by = c("pathway" = "label")) %>%
  dplyr::rename(from = id) %>%
  left_join(nodesWiki, by = c("gene" = "label")) %>%
  dplyr::rename(to = id) %>%
  dplyr::select(from, to)

# Create tidygraph object
tidyWiki <- 
  tbl_graph(nodes = nodesWiki, edges = edgesWiki, directed = FALSE) %>%
  activate(nodes) %>%
  left_join(idConvertSymbol, by = "label") %>%
  mutate(
    pathways = case_when(
      id <= nrow(fgseaWikiTop) ~ label
    ),
    DE = case_when(
      label %in% topTableDE$Geneid ~ symbol
    ),
    size = case_when(
      label %in% topTable$Geneid ~ 
        as.integer(row_number(label %in% topTable$Geneid)), 
      id <= nrow(fgseaWikiTop) ~ as.integer(4000)
    ),
    colour = case_when(
      id <= nrow(fgseaWikiTop) ~ rainbow(nrow(fgseaWikiTop))[id],
      label %in% topTableDE$Geneid ~ "black"
    ),
    hjust = case_when(
      DE == "ugt5b4" ~ as.integer(1),
      DE == "blvrb" ~ as.integer(2)
    ),
    vjust = case_when(
      DE == "ugt5b4" ~ as.integer(7),
      DE == "blvrb" ~ as.integer(7)
    )
  ) %>%
  activate(edges) %>%
  mutate(
    colour = case_when(
      from <= nrow(fgseaWikiTop) ~ rainbow(nrow(fgseaWikiTop))[from]
    )
  )
# Set seed to enable reproducibility (seed selected to create graph with non-overlapping labels)
set.seed(27)
# Plot graph
ggraph(tidyWiki, layout = "fr") +
  scale_fill_manual(
    values = c(rainbow(nrow(fgseaWikiTop)), "black"), 
    na.value = "gray80"
  ) +
  geom_edge_arc(
    aes(color = colour), 
    alpha = 0.5, 
    show.legend = FALSE, 
    curvature = 0.5
  ) +
  geom_node_point(
    aes(size = size, fill = colour), 
    shape = 21, 
    stroke = 0.5, 
    show.legend = FALSE
  ) +
  geom_node_label(
    aes(label = pathways), 
    repel = TRUE, 
    size = 3, 
    alpha = 0.7, 
    label.padding = 0.1
  ) +
  geom_node_text(
    aes(label = DE, hjust = hjust, vjust = vjust), 
    repel = TRUE, 
    size = 3, 
    alpha = 0.8, 
    colour = "black"
  ) +
  theme_graph() +
  theme(legend.position = "none")

RUVSeq

k = 1

# Extract subset of low expressed genes from DE analysis to act as negative controls for RUVg procedure
negControl <- topTable %>% 
  dplyr::arrange(desc(P.Value)) %>%
  .[1:10000,] %>%
  .$Geneid

# Run RUVSeq
RUVk1 <- RUVg(dgeFilt$counts, negControl, 1)

# Create copy of dgeFilt as framework to replace RUVSeq results into
dgeRUVk1 <- dgeFilt
# Replace with results
dgeRUVk1$counts <- RUVk1$normalizedCounts

# Run PCA function
pcaRUVk1 <- dgeRUVk1 %>%
  cpm(log = TRUE) %>%
  t() %>%
  prcomp()
# Quick inspection to check whether first two PCA components capture most of the variability
summary(pcaRUVk1)$importance %>% pander(split.tables = Inf)

	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8	PC9
Standard deviation	20.11	18.01	16.48	15.75	14.34	13.36	12.55	11.28	7.413e-14
Proportion of Variance	0.2109	0.1691	0.1417	0.1294	0.1073	0.09307	0.0822	0.06639	0
Cumulative Proportion	0.2109	0.38	0.5217	0.6511	0.7583	0.8514	0.9336	1	1

# Plot PCA
pcak1 <- pcaRUVk1$x %>%
  as.data.frame() %>%
  rownames_to_column("sample") %>%
  as_tibble() %>%
  dplyr::select(sample, PC1, PC2) %>%
  left_join(rownames_to_column(dgeRUVk1$samples, "sample")) %>%
  ggplot(aes(PC1, PC2, colour = group, label = sample)) +
  geom_point() +
  geom_text_repel(show.legend = FALSE) +
  theme_bw()

k = 2

# Run RUVSeq
RUVk2 <- RUVg(dgeFilt$counts, negControl, 2)

# Create copy of dgeFilt as framework to replace RUVSeq results into
dgeRUVk2 <- dgeFilt
# Replace with results
dgeRUVk2$counts <- RUVk2$normalizedCounts

# Run PCA function
pcaRUVk2 <- dgeRUVk2 %>%
  cpm(log = TRUE) %>%
  t() %>%
  prcomp()
# Quick inspection to check whether first two PCA components capture most of the variability
summary(pcaRUVk2)$importance %>% pander(split.tables = Inf)

	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8	PC9
Standard deviation	18.38	17.65	16.58	14.48	13.37	12.59	11.27	0.4422	4.728e-14
Proportion of Variance	0.2113	0.1949	0.1721	0.1311	0.1119	0.09915	0.07951	0.00012	0
Cumulative Proportion	0.2113	0.4062	0.5782	0.7093	0.8212	0.9204	0.9999	1	1

# Plot PCA
pcak2 <- pcaRUVk2$x %>%
  as.data.frame() %>%
  rownames_to_column("sample") %>%
  as_tibble() %>%
  dplyr::select(sample, PC1, PC2) %>%
  left_join(rownames_to_column(dgeRUVk2$samples, "sample")) %>%
  ggplot(aes(PC1, PC2, colour = group, label = sample)) +
  geom_point() +
  geom_text_repel(show.legend = FALSE) +
  theme_bw()

Results

# Set up grid for visualisation of 2 PCA's at once.
vp1 <- viewport(x = 0.25, y = 0.5, width = 0.5, height = 1)
vp2 <- viewport(x = 0.75, y = 0.5, width = 0.5, height = 1)
# Plot PCA plots in grid
if (interactive()) grid::grid.newpage()
print(pcak1, vp = vp1)
print(pcak2, vp = vp2)

2017 data analysis

Differential Expression Analysis

Load data

kalCounts <- read_rds("nhiData/6month_Normoxia.rds")
dgeListNhi <- kalCounts$counts %>%
  as.data.frame() %>%
  rownames_to_column("tx_id") %>%
  as_tibble() %>%
  set_colnames(basename(colnames(.))) %>%
  mutate(tx_id = str_remove_all(tx_id, "\\.[0-9]*$")) %>%
  left_join(trans2Gene) %>%
  group_by(gene_id) %>%
  summarise_if(
    .predicate = is.numeric,
    .funs = sum
    ) %>%
  as.data.frame() %>%
  column_to_rownames("gene_id") %>%
  round() %>%
  set_colnames(str_remove(colnames(.), "[0-9]_MORGAN_")) %>%
  set_colnames(str_remove(colnames(.), "PN")) %>%
  set_colnames(str_remove(colnames(.), "_[RS].+")) %>%
  set_colnames(str_replace(colnames(.), "6P", "6W")) %>%
  DGEList(
    group = colnames(.) %>%
      str_replace_all("(6)([WQ])(.+)", "\\2") %>%
      str_replace_all("W", "WT") %>%
      str_replace_all("Q", "Mut") %>%
      factor(levels = c("WT", "Mut")),
    genes = genesGR[rownames(.)]
  ) %>%
  calcNormFactors() 

Data QC

# Perform logical test to see how many genes were not detected in dataset
dgeListNhi$counts %>%
  rowSums() %>%
  is_greater_than(0) %>%
  table()

## .
## FALSE  TRUE 
##  1282 24299

# Check for genes having >= 4 samples with cpm > 1
dgeListNhi %>%
  cpm() %>%
  is_greater_than(1) %>%
  rowSums() %>%
  is_weakly_greater_than(4) %>%
  table()

## .
## FALSE  TRUE 
##  7049 18532

# Create logical vector of genes to keep that fit criteria
genes2keepNhi <- dgeListNhi %>%
  cpm() %>%
  is_greater_than(1) %>%
  rowSums() %>%
  is_weakly_greater_than(4)

# Create new DGEList of genes fitting criteria
dgeFiltNhi <- dgeListNhi[genes2keepNhi,, keep.lib.sizes = FALSE] %>%
  calcNormFactors()

# Compare distributions of the DGELists before and after filtering
par(mfrow = c(1,2))
dgeListNhi %>%
  cpm(log = TRUE) %>%
  plotDensities(legend = FALSE, main = "Before Filtering")
dgeFiltNhi %>%
  cpm(log = TRUE) %>%
  plotDensities(legend = FALSE, main = "After Filtering")

par(mfrow = c(1,1))

Library sizes

# Check library sizes with box plot
dgeFiltNhi$samples %>%
  ggplot(aes(group, lib.size, fill = group)) +
  geom_boxplot() +
  scale_y_continuous(labels = comma) +
  labs(x = "Genotype", y = "Library Size") +
  scale_fill_discrete(
    name ="Genotype", 
    labels = c("Wildtype","Mutant")
  ) +
  scale_x_discrete(labels=c("w" = "Wildtype", "q" = "Mutant")) +
  theme_bw()

PCA

# Assess cpm values to make sure PCA results are not heavily skewed by highly expressed genes
pcaNhi <- dgeFiltNhi %>%
  cpm(log = TRUE) %>%
  t() %>%
  prcomp()

# Quick inspection to check whether first two PCA components capture most of the variability
summary(pcaNhi)$importance %>% pander(split.tables = Inf)

	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8
Standard deviation	18.02	16.09	15.83	14.74	14.21	13.33	13.09	4.364e-14
Proportion of Variance	0.2026	0.1616	0.1565	0.1356	0.1261	0.1108	0.1069	0
Cumulative Proportion	0.2026	0.3642	0.5206	0.6562	0.7823	0.8931	1	1

# Plot PCA
pcaNhi$x %>%
  as.data.frame() %>%
  rownames_to_column("sample") %>%
  as_tibble() %>%
  dplyr::select(sample, PC1, PC2) %>%
  left_join(rownames_to_column(dgeFiltNhi$samples, "sample")) %>%
  ggplot(aes(PC1, PC2, colour = group, label = sample)) +
  geom_point() +
  geom_text_repel(show.legend = FALSE) +
  theme_bw()

Differential expression

# Create model matrix
design <- model.matrix(~group, data = dgeFiltNhi$samples)

# Perform exact test on DGEList
topTableNhi <- dgeFiltNhi %>%
  estimateDisp(design = design) %>%
  exactTest() %>%
  topTags(n = Inf) %>%
  .$table %>%
  as_tibble() %>%
  unite("Range", start, end, sep = "-") %>%
  unite("Location", seqnames, Range, strand, sep = ":") %>%
  dplyr::select(Geneid = gene_id, 
                Symbol = gene_name,
                AveExpr = logCPM, logFC, 
                P.Value = PValue, 
                FDR, Location, 
                Entrez = entrezid) %>%
  mutate(DE = FDR < 0.05)

# Volcano plot showing DE genes
topTableNhi %>%
  ggplot(aes(logFC, -log10(P.Value), colour = DE)) +
  geom_point(alpha = 0.5) +
  geom_text_repel(data = . %>% 
                    dplyr::filter(DE) %>%
                    dplyr::filter(-log10(P.Value) > 4 | abs(logFC) >                        2.5), aes(label = Symbol)) + 
  scale_colour_manual(values = c("grey", "red")) +
  theme_bw() +
  theme(legend.position = "none")

# MD Plot showing DE genes
topTableNhi %>%
  dplyr::arrange(desc(P.Value)) %>%
  ggplot(aes(AveExpr, logFC, colour = DE)) +
  geom_point(alpha = 0.5) +
  geom_text_repel(data = . %>% 
                    dplyr::filter(DE) %>%
                    dplyr::filter(abs(logFC) > 2 | AveExpr > 14),
                  aes(label = Symbol)) + 
  scale_colour_manual(values = c("grey", "red")) +
  labs(x = "Average Expression (log2 CPM)",
       y = "log Fold-Change") +
  theme_bw() +
  theme(legend.position = "none")

# Summary of DE genes
topTableDENhi <- topTableNhi %>%
  dplyr::filter(FDR < 0.05) %>%
  dplyr::select(Geneid, Symbol, AveExpr, logFC, P.Value, FDR) 
topTableDENhi %>% pander(style = "rmarkdown", split.tables = Inf)

Geneid	Symbol	AveExpr	logFC	P.Value	FDR
ENSDARG00000087290	si:ch211-202h22.10	0.6292	5.598	9.788e-23	1.814e-18
ENSDARG00000077567	angel1	3.237	-0.7363	7.88e-09	7.301e-05
ENSDARG00000058270	COLGALT1 (1 of many)	5.742	0.5835	2.138e-07	0.001182
ENSDARG00000102796	si:ch1073-83b15.1	1.702	1.163	2.612e-07	0.001182
ENSDARG00000101407	tgm1l4	0.205	-1.664	3.188e-07	0.001182
ENSDARG00000092604	si:ch211-15j1.4	4.745	1.068	2.306e-06	0.007022
ENSDARG00000074266	si:ch1073-314i13.4	6.159	-0.437	3.105e-06	0.007022
ENSDARG00000103727	tfr2	0.8383	-1.198	3.348e-06	0.007022
ENSDARG00000035871	rpl30	6.328	-0.295	3.41e-06	0.007022
ENSDARG00000098688	CABZ01101739.1	3.57	0.5685	6.214e-06	0.01152
ENSDARG00000045248	h3f3d	6.362	-0.3339	1.18e-05	0.01988
ENSDARG00000104839	mansc1	5.318	0.4417	1.694e-05	0.02616
ENSDARG00000075759	samd4a	6.597	0.3786	2.113e-05	0.02805
ENSDARG00000091209	ucp3	0.5011	-1.314	2.119e-05	0.02805
ENSDARG00000094408	MFAP4 (1 of many)	0.1877	-1.82	2.519e-05	0.03112
ENSDARG00000032005	ccdc65	2.008	0.7997	3.23e-05	0.03741
ENSDARG00000096375	march7	6.999	0.3083	3.59e-05	0.03913
ENSDARG00000024847	col5a2b	1.45	-0.9456	4.18e-05	0.04112
ENSDARG00000092807	RPL41	6.716	-0.2908	4.215e-05	0.04112
ENSDARG00000091025	znf1124	0.816	1.289	5.094e-05	0.0472

Gene Set Enrichment Analysis (GSEA)

# Create named vector of gene level statistics 
ranksNhi <- topTableNhi %>%
  mutate(stat = -sign(logFC) * log10(P.Value)) %>%
  dplyr::arrange(desc(stat)) %>%
  with(structure(stat, names = Geneid))

Hallmark

# Set seed to enable reproducibility
set.seed(22)
# Run GSEA for hallmark
fgseaHallmarkNhi <- fgsea(hallmark, ranksNhi, nperm=1e5) %>%
  as_tibble() %>%
  dplyr::rename(FDR = padj) %>%
  mutate(padj = p.adjust(pval, "bonferroni")) %>%
  dplyr::arrange(pval)
# Create an object of pathways with adjusted p-value < 0.05 for construction of network diagrams. This should be done differently next time, but too much work has been done to change it now.
fgseaHallmarkNhiTop <- fgseaHallmarkNhi %>%
  dplyr::filter(padj < 0.05)
fgseaHallmarkNhiTop %>%
  dplyr::select(-leadingEdge, -nMoreExtreme) %>%
  pander(
    style = "rmarkdown", 
    split.tables = Inf, 
    justify = "lrrrrrr", 
    caption = paste(
      "The", nrow(.), "most significantly enriched Hallmark pathways.",
      "This corresponds to an FDR of", percent(max(.$FDR)))
  )

The 4 most significantly enriched Hallmark pathways. This corresponds to an FDR of 0.780%

pathway	pval	FDR	ES	NES	size	padj
OXIDATIVE_PHOSPHORYLATION	2.011e-05	0.001006	-0.4286	-1.738	222	0.001006
MTORC1_SIGNALING	0.0001007	0.002517	-0.4053	-1.648	229	0.005035
MYC_TARGETS_V1	0.0005627	0.007796	-0.3841	-1.557	221	0.02813
XENOBIOTIC_METABOLISM	0.0006237	0.007796	-0.3829	-1.553	223	0.03118

# Make a table plot of significant Hallmark pathways
if (interactive()) grid::grid.newpage()
plotGseaTable(hallmark[fgseaHallmarkNhi$pathway], 
              ranksNhi, fgseaHallmarkNhi, gseaParam = 0.5)

KEGG

# Set seed to enable reproducibility
set.seed(22)
# Run GSEA for hallmark
fgseaKEGGNhi <- fgsea(kegg, ranksNhi, nperm=1e5) %>%
  as_tibble() %>%
  dplyr::rename(FDR = padj) %>%
  mutate(padj = p.adjust(pval, "bonferroni")) %>%
  dplyr::arrange(pval)
# Create an object of pathways with adjusted p-value < 0.05 for construction of network diagrams. This should be done differently next time, but too much work has been done to change it now.
fgseaKEGGNhiTop <- fgseaKEGGNhi %>%
  dplyr::filter(padj < 0.05)
fgseaKEGGNhiTop %>%
  dplyr::select(-leadingEdge, -nMoreExtreme) %>%
  pander(
    style = "rmarkdown", 
    split.tables = Inf, 
    justify = "lrrrrrr", 
    caption = paste(
      "The", nrow(.), "most significantly enriched KEGG pathways.",
      "This corresponds to an FDR of", percent(max(.$FDR)))
  )

The 8 most significantly enriched KEGG pathways. This corresponds to an FDR of 0.0924%

pathway	pval	FDR	ES	NES	size	padj
FATTY_ACID_METABOLISM	1.985e-05	0.0005352	-0.705	-2.447	73	0.003692
TYROSINE_METABOLISM	1.986e-05	0.0005352	-0.7169	-2.447	66	0.003694
RIBOSOME	1.988e-05	0.0005352	-0.8126	-2.87	81	0.003698
GLYCOLYSIS_GLUCONEOGENESIS	1.993e-05	0.0005352	-0.6286	-2.271	94	0.003708
METABOLISM_OF_XENOBIOTICS_BY_CYTOCHROME_P450	2.014e-05	0.0005352	-0.4192	-1.738	274	0.003745
DRUG_METABOLISM_CYTOCHROME_P450	2.014e-05	0.0005352	-0.4515	-1.867	268	0.003746
RETINOL_METABOLISM	2.014e-05	0.0005352	-0.4822	-1.997	273	0.003746
PEROXISOME	3.973e-05	0.0009238	-0.5632	-1.981	79	0.00739

# Make a table plot of significant Hallmark pathways
if (interactive()) grid::grid.newpage()
plotGseaTable(kegg[fgseaKEGGNhi$pathway], 
              ranksNhi, fgseaKEGGNhi, gseaParam = 0.5)

Wikipathways

# Set seed to enable reproducibility
set.seed(22)
# Run GSEA for hallmark
fgseaWikiNhi <- fgsea(wiki, ranksNhi, nperm=1e5) %>%
  as_tibble() %>%
  dplyr::rename(FDR = padj) %>%
  mutate(padj = p.adjust(pval, "bonferroni")) %>%
  dplyr::arrange(pval)
# Create an object of pathways with adjusted p-value < 0.05 for construction of network diagrams. This should be done differently next time, but too much work has been done to change it now.
fgseaWikiNhiTop <- fgseaWikiNhi %>%
  dplyr::filter(padj < 0.05)
fgseaWikiNhiTop %>%
  dplyr::select(-leadingEdge, -nMoreExtreme) %>%
  pander(
    style = "rmarkdown", 
    split.tables = Inf, 
    justify = "lrrrrrr", 
    caption = paste(
      "The", nrow(.), "most significantly enriched Wiki pathways.",
      "This corresponds to an FDR of", percent(max(.$FDR)))
  )

The 3 most significantly enriched Wiki pathways. This corresponds to an FDR of 0.350%

pathway	pval	FDR	ES	NES	size	padj
Cytoplasmic Ribosomal Proteins	1.995e-05	0.003503	-0.8051	-2.838	81	0.01045
Fatty Acid Omega Oxidation	2.003e-05	0.003503	-0.8832	-2.609	32	0.0105
Ethanol effects on histone modifications	2.006e-05	0.003503	-0.6779	-2.088	39	0.01051

# Make a table plot of significant Hallmark pathways
if (interactive()) grid::grid.newpage()
plotGseaTable(wiki[fgseaWikiNhi$pathway], 
              ranksNhi, fgseaWikiNhi, gseaParam = 0.5)

Network Analysis

Hallmark

# Load significant pathways with ONLY leading edge genes determined from GSEA analysis
sigHallmarkNhi <-
  fgseaHallmarkNhiTop %>%
  split(f = .$pathway) %>% 
  lapply(extract2, "leadingEdge") %>% 
  lapply(unlist)

# Create a node list
pathwaysHallmarkNhi <- names(sigHallmarkNhi) %>%
  as.data.frame() %>%
  set_colnames("label") %>%
  mutate(label = as.character(label))
genesHallmarkNhi <- unique(unlist(sigHallmarkNhi)) %>% 
  as.data.frame() %>% 
  set_colnames("label") %>%
  mutate(label = as.character(label))
nodesHallmarkNhi <- full_join(pathwaysHallmarkNhi, genesHallmarkNhi, by = "label") %>%
  rowid_to_column("id")
# Then create an edge list
edgesHallmarkNhi <- ldply(sigHallmarkNhi, data.frame) %>% 
  set_colnames(c("pathway", "gene")) %>%
  mutate(gene = as.character(gene)) %>%
  left_join(nodesHallmarkNhi, by = c("pathway" = "label")) %>%
  dplyr::rename(from = id) %>%
  left_join(nodesHallmarkNhi, by = c("gene" = "label")) %>%
  dplyr::rename(to = id) %>%
  dplyr::select(from, to)

# Create tidygraph object
tidyHallmarkNhi <- 
  tbl_graph(nodes = nodesHallmarkNhi, 
            edges = edgesHallmarkNhi, 
            directed = FALSE) %>%
  activate(nodes) %>%
  left_join(idConvertSymbol, by = "label") %>%
  mutate(
    pathways = case_when(
      id <= nrow(fgseaHallmarkNhiTop) ~ label
    ),
    DE = case_when(
      label %in% topTableDENhi$Geneid ~ symbol
    ),
    size = case_when(
      label %in% topTableNhi$Geneid ~ 
        as.integer(row_number(label %in% topTableNhi$Geneid)), 
      id <= nrow(fgseaHallmarkNhiTop) ~ as.integer(4000)
    ),
    colour = case_when(
      id <= nrow(fgseaHallmarkNhiTop) ~ rainbow(nrow(fgseaHallmarkNhiTop))[id],
      label %in% topTableDENhi$Geneid ~ "black"
    )
  ) %>%
  activate(edges) %>%
  mutate(
    colour = case_when(
      from <= nrow(fgseaHallmarkNhiTop) ~ 
        rainbow(nrow(fgseaHallmarkNhiTop))[from]
    )
  )
# Set seed to enable reproducibility (seed selected to create graph with non-overlapping labels)
set.seed(22)
# Plot graph
ggraph(tidyHallmarkNhi, layout = "fr") +
  scale_fill_manual(
    values = c(rainbow(nrow(fgseaHallmarkNhiTop)), "black"), 
    na.value = "gray80"
  ) +
  geom_edge_arc(
    aes(color = colour),
    alpha = 0.5, 
    show.legend = FALSE, 
    curvature = 0.5
  ) +
  geom_node_point(
    aes(size = size, fill = colour), 
    shape = 21, stroke = 0.5, 
    show.legend = FALSE
  ) +
  geom_node_label(
    aes(label = pathways), 
    repel = TRUE, size = 3, 
    alpha = 0.7, 
    label.padding = 0.1
  ) +
  geom_node_text(
    aes(label = DE), 
    repel = TRUE, 
    size = 3, 
    alpha = 0.8, 
    colour = "black"
  ) +
  theme_graph() +
  theme(legend.position = "none")

KEGG

# Load significant pathways with ONLY leading edge genes determined from GSEA analysis
sigKEGGNhi <-
  fgseaKEGGNhiTop %>%
  split(f = .$pathway) %>% 
  lapply(extract2, "leadingEdge") %>% 
  lapply(unlist)

# Create a node list
pathwaysKEGGNhi <- names(sigKEGGNhi) %>%
  as.data.frame() %>%
  set_colnames("label") %>%
  mutate(label = as.character(label))
genesKEGGNhi <- unique(unlist(sigKEGGNhi)) %>% 
  as.data.frame() %>% 
  set_colnames("label") %>%
  mutate(label = as.character(label))
nodesKEGGNhi <- full_join(pathwaysKEGGNhi, genesKEGGNhi, by = "label") %>%
  rowid_to_column("id")
# Then create an edge list
edgesKEGGNhi <- ldply(sigKEGGNhi, data.frame) %>% 
  set_colnames(c("pathway", "gene")) %>%
  mutate(gene = as.character(gene)) %>%
  left_join(nodesKEGGNhi, by = c("pathway" = "label")) %>%
  dplyr::rename(from = id) %>%
  left_join(nodesKEGGNhi, by = c("gene" = "label")) %>%
  dplyr::rename(to = id) %>%
  dplyr::select(from, to)

# Create tidygraph object
tidyKEGGNhi <- 
  tbl_graph(nodes = nodesKEGGNhi, edges = edgesKEGGNhi, directed = FALSE) %>%
  activate(nodes) %>%
  left_join(idConvertSymbol, by = "label") %>%
  mutate(
    pathways = case_when(
      id <= nrow(fgseaKEGGNhiTop) ~ label
    ),
    DE = case_when(
      label %in% topTableDENhi$Geneid ~ symbol
    ),
    size = case_when(
      label %in% topTableNhi$Geneid ~ 
        as.integer(row_number(label %in% topTableNhi$Geneid)), 
      id <= nrow(fgseaKEGGNhiTop) ~ as.integer(4000)
    ),
    colour = case_when(
      id <= nrow(fgseaKEGGNhiTop) ~ rainbow(nrow(fgseaKEGGNhiTop))[id],
      label %in% topTableDENhi$Geneid ~ "black"
    )
  ) %>%
  activate(edges) %>%
  mutate(
    colour = case_when(
      from <= nrow(fgseaKEGGNhiTop) ~ rainbow(nrow(fgseaKEGGNhiTop))[from]
    )
  )
# Set seed to enable reproducibility (seed selected to create graph with non-overlapping labels)
set.seed(22)
# Plot graph
ggraph(tidyKEGGNhi, layout = "fr") +
  scale_fill_manual(
    values = c(rainbow(nrow(fgseaKEGGNhiTop)), "black"), 
    na.value = "gray80"
  ) +
  geom_edge_arc(
    aes(color = colour), 
    alpha = 0.5, 
    show.legend = FALSE, 
    curvature = 0.5
  ) +
  geom_node_point(
    aes(size = size, fill = colour), 
    shape = 21, 
    stroke = 0.5, 
    show.legend = FALSE
  ) +
  geom_node_label(
    aes(label = pathways), 
    repel = TRUE, 
    size = 3, 
    alpha = 0.7, 
    label.padding = 0.1
  ) +
  geom_node_text(
    aes(label = DE), 
    repel = TRUE, 
    size = 3, 
    alpha = 0.8, 
    colour = "black"
  ) +
  theme_graph() +
  theme(legend.position = "none")

Wikipathways

# Load significant pathways with ONLY leading edge genes determined from GSEA analysis
sigWikiNhi <-
  fgseaWikiNhiTop %>%
  split(f = .$pathway) %>% 
  lapply(extract2, "leadingEdge") %>% 
  lapply(unlist)

# Create a node list
pathwaysWikiNhi <- names(sigWikiNhi) %>%
  as.data.frame() %>%
  set_colnames("label") %>%
  mutate(label = as.character(label))
genesWikiNhi <- unique(unlist(sigWikiNhi)) %>% 
  as.data.frame() %>% 
  set_colnames("label") %>%
  mutate(label = as.character(label))
nodesWikiNhi <- full_join(pathwaysWikiNhi, genesWikiNhi, by = "label") %>%
  rowid_to_column("id")
# Then create an edge list
edgesWikiNhi <- ldply(sigWikiNhi, data.frame) %>% 
  set_colnames(c("pathway", "gene")) %>%
  mutate(gene = as.character(gene)) %>%
  left_join(nodesWikiNhi, by = c("pathway" = "label")) %>%
  dplyr::rename(from = id) %>%
  left_join(nodesWikiNhi, by = c("gene" = "label")) %>%
  dplyr::rename(to = id) %>%
  dplyr::select(from, to)

# Create tidygraph object
tidyWikiNhi <- 
  tbl_graph(nodes = nodesWikiNhi, edges = edgesWikiNhi, directed = FALSE) %>%
  activate(nodes) %>%
  left_join(idConvertSymbol, by = "label") %>%
  mutate(
    pathways = case_when(
      id <= nrow(fgseaWikiNhiTop) ~ label
    ),
    DE = case_when(
      label %in% topTableDENhi$Geneid ~ symbol
    ),
    size = case_when(
      label %in% topTableNhi$Geneid ~ 
        as.integer(row_number(label %in% topTableNhi$Geneid)), 
      id <= nrow(fgseaWikiNhiTop) ~ as.integer(4000)
    ),
    colour = case_when(
      id <= nrow(fgseaWikiNhiTop) ~ rainbow(nrow(fgseaWikiNhiTop))[id],
      label %in% topTableDENhi$Geneid ~ "black"
    )
  ) %>%
  activate(edges) %>%
  mutate(
    colour = case_when(
      from <= nrow(fgseaWikiNhiTop) ~ rainbow(nrow(fgseaWikiNhiTop))[from]
    )
  )
# Set seed to enable reproducibility (seed selected to create graph with non-overlapping labels)
set.seed(22)
# Plot graph
ggraph(tidyWikiNhi, layout = "fr") +
  scale_fill_manual(
    values = c(rainbow(nrow(fgseaWikiNhiTop)), "black"), 
    na.value = "gray80"
  ) +
  geom_edge_arc(
    aes(color = colour),
    alpha = 0.5, 
    show.legend = FALSE, 
    curvature = 0.5
  ) +
  geom_node_point(
    aes(size = size, fill = colour), 
    shape = 21, 
    stroke = 0.5, 
    show.legend = FALSE
  ) +
  geom_node_label(
    aes(label = pathways), 
    repel = TRUE, 
    size = 3, 
    alpha = 0.7, 
    label.padding = 0.1
  ) +
  geom_node_text(
    aes(label = DE), 
    repel = TRUE, 
    size = 3, 
    alpha = 0.8, 
    colour = "black") +
  theme_graph() +
  theme(legend.position = "none")

RUVSeq

k = 1

# Extract subset of low expressed genes from DE analysis to act as negative controls for RUVg procedure
negControlNhi <- topTableNhi %>% 
  dplyr::arrange(desc(P.Value)) %>%
  .[1:10000,] %>%
  .$Geneid

# Run RUVSeq
RUVk1Nhi <- RUVg(dgeFiltNhi$counts, negControlNhi, 1)

# Create copy of dgeFilt as framework to replace RUVSeq results into
dgeRUVk1Nhi <- dgeFiltNhi
# Replace with results
dgeRUVk1Nhi$counts <- RUVk1Nhi$normalizedCounts

# Run PCA function
pcaRUVk1Nhi <- dgeRUVk1Nhi %>%
  cpm(log = TRUE) %>%
  t() %>%
  prcomp()
# Quick inspection to check whether first two PCA components capture most of the variability
summary(pcaRUVk1Nhi)$importance %>% pander(split.tables = Inf)

	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8
Standard deviation	41.44	17.75	15.99	15.72	14.74	13.86	13.2	7.664e-14
Proportion of Variance	0.5505	0.1011	0.08196	0.07922	0.06968	0.06164	0.0559	0
Cumulative Proportion	0.5505	0.6516	0.7336	0.8128	0.8825	0.9441	1	1

# Plot PCA
pcak1Nhi <- pcaRUVk1Nhi$x %>%
  as.data.frame() %>%
  rownames_to_column("sample") %>%
  as_tibble() %>%
  dplyr::select(sample, PC1, PC2) %>%
  left_join(rownames_to_column(dgeRUVk1Nhi$samples, "sample")) %>%
  ggplot(aes(PC1, PC2, colour = group, label = sample)) +
  geom_point() +
  geom_text_repel(show.legend = FALSE) +
  theme_bw()

k = 2

# Run RUVSeq
RUVk2Nhi <- RUVg(dgeFiltNhi$counts, negControlNhi, 2)

# Create copy of dgeFilt as framework to replace RUVSeq results into
dgeRUVk2Nhi <- dgeFiltNhi
# Replace with results
dgeRUVk2Nhi$counts <- RUVk2Nhi$normalizedCounts

# Run PCA function
pcaRUVk2Nhi <- dgeRUVk2Nhi %>%
  cpm(log = TRUE) %>%
  t() %>%
  prcomp()
# Quick inspection to check whether first two PCA components capture most of the variability
summary(pcaRUVk2Nhi)$importance %>% pander(split.tables = Inf)

	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8
Standard deviation	41.44	16.29	15.84	14.86	13.96	13.25	0.5056	4.734e-14
Proportion of Variance	0.6078	0.09397	0.08881	0.07817	0.069	0.06213	9e-05	0
Cumulative Proportion	0.6078	0.7018	0.7906	0.8688	0.9378	0.9999	1	1

# Plot PCA
pcak2Nhi <- pcaRUVk2Nhi$x %>%
  as.data.frame() %>%
  rownames_to_column("sample") %>%
  as_tibble() %>%
  dplyr::select(sample, PC1, PC2) %>%
  left_join(rownames_to_column(dgeRUVk2Nhi$samples, "sample")) %>%
  ggplot(aes(PC1, PC2, colour = group, label = sample)) +
  geom_point() +
  geom_text_repel(show.legend = FALSE) +
  theme_bw()

Results

# Set up grid for visualisation of 2 PCA's at once.
vp1 <- viewport(x = 0.25, y = 0.5, width = 0.5, height = 1)
vp2 <- viewport(x = 0.75, y = 0.5, width = 0.5, height = 1)
# Plot PCA plots in grid
if (interactive()) grid::grid.newpage()
print(pcak1Nhi, vp = vp1)
print(pcak2Nhi, vp = vp2)

Integration of 2019 data with 2017 data

LogFC Comparison

Meta Analysis

Hallmark

# Extract p-values from 2017 and 2019 GSEA results and combine using Fisher's method
p2017Hallmark <- fgseaHallmarkNhi %>%
  dplyr::select(pathway, pval) %>%
  dplyr::mutate(pval = log10(pval)) %>%
  dplyr::rename(log10p2017 = pval)
p2019Hallmark <- fgseaHallmark %>%
  dplyr::select(pathway, pval) %>%
  dplyr::mutate(pval = log10(pval)) %>%
  dplyr::rename(log10p2019 = pval)
fgseaHallmarkMeta <- full_join(p2017Hallmark, p2019Hallmark) %>%
  replace(is.na(.), 0) %>%
  dplyr::mutate(
    chiSquare = -2 * (log10p2017 + log10p2019),
    pCombined = pchisq(chiSquare, df = 4, lower.tail = FALSE),
    FDR = p.adjust(pCombined, "fdr")
  ) %>%
  dplyr::arrange(pCombined)

KEGG

# Extract p-values from 2017 and 2019 GSEA results and combine using Fisher's method
p2017KEGG <- fgseaKEGGNhi %>%
  dplyr::select(pathway, pval) %>%
  dplyr::mutate(pval = log10(pval)) %>%
  dplyr::rename(log10p2017 = pval)
p2019KEGG <- fgseaKEGG %>%
  dplyr::select(pathway, pval) %>%
  dplyr::mutate(pval = log10(pval)) %>%
  dplyr::rename(log10p2019 = pval)
fgseaKEGGMeta <- full_join(p2017KEGG, p2019KEGG) %>%
  replace(is.na(.), 0) %>%
  dplyr::mutate(
    chiSquare = -2 * (log10p2017 + log10p2019),
    pCombined = pchisq(chiSquare, df = 4, lower.tail = FALSE),
    FDR = p.adjust(pCombined, "fdr")
  ) %>%
  dplyr::arrange(pCombined)

Results

# Hallmark
fgseaHallmarkMeta %>%
  dplyr::filter(FDR < 0.05) %>%
  pander(
    style = "rmarkdown", 
    split.tables = Inf, 
    justify = "lrrrrr", 
    caption = paste(
      "The", nrow(.), "most significantly enriched hallmark pathways.",
      "This corresponds to an FDR of", percent(max(.$FDR)))
  )

The 1 most significantly enriched hallmark pathways. This corresponds to an FDR of 3.58%

pathway	log10p2017	log10p2019	chiSquare	pCombined	FDR
OXIDATIVE_PHOSPHORYLATION	-4.697	-4.908	19.21	0.0007152	0.03576

# KEGG
fgseaKEGGMeta %>%
  dplyr::filter(FDR < 0.05) %>%
  pander(
    style = "rmarkdown", 
    split.tables = Inf, 
    justify = "lrrrrr", 
    caption = paste(
      "The", nrow(.), "most significantly enriched KEGG pathways.",
      "This corresponds to an FDR of", percent(max(.$FDR)))
  )

The 0 most significantly enriched KEGG pathways. This corresponds to an FDR of -Inf

pathway

log10p2017

log10p2019

chiSquare

pCombined

FDR

PCA

Set up DGE List and QC data

# Combine counts into the same DGE List
# First extract counts from 2017 DGE List
countsNhi <- dgeListNhi$counts %>%
  as.data.frame() %>%
  rownames_to_column("Geneid") %>%
  as_tibble()
# Then join with 2019 counts
dgeListComb <- full_join(counts, countsNhi) %>%
  replace(is.na(.), 0) %>%
  as.data.frame() %>%
  column_to_rownames("Geneid") %>%
  DGEList() %>%
  calcNormFactors()
# Set group variable
dgeListComb$samples$group <- colnames(dgeListComb) %>%
  str_extract("(W|Q)") %>%
  factor(levels = c("W", "Q"))

# Create logical vector of genes to keep that fit criteria
genes2keepComb <- dgeListComb %>%
  cpm() %>%
  is_greater_than(1) %>%
  rowSums() %>%
  is_weakly_greater_than(8)

# Create new DGEList of genes fitting criteria
dgeFiltComb <- dgeListComb[genes2keepComb,, 
                                   keep.lib.sizes = FALSE] %>%
  calcNormFactors()

# Compare distributions of the DGELists before and after filtering
par(mfrow = c(1,2))
dgeListComb %>%
  cpm(log = TRUE) %>%
  plotDensities(legend = FALSE, main = "Before Filtering")
dgeFiltComb %>%
  cpm(log = TRUE) %>%
  plotDensities(legend = FALSE, main = "After Filtering")

par(mfrow = c(1,1))

Plot PCA graphs

# Assess cpm values to make sure PCA results are not heavily skewed by highly expressed genes
pcaComb <- dgeFiltComb %>%
  cpm(log = TRUE) %>%
  t() %>%
  prcomp()

# Quick inspection to check whether first two PCA components capture most of the variability
summary(pca)$importance %>% pander(split.tables = Inf)

	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8	PC9
Standard deviation	22.27	18.07	16.75	14.73	14.45	13.34	11.87	11.2	5.671e-14
Proportion of Variance	0.2513	0.1655	0.1421	0.1099	0.1058	0.09023	0.07145	0.06362	0
Cumulative Proportion	0.2513	0.4168	0.559	0.6689	0.7747	0.8649	0.9364	1	1

# Create PCA plots
pca12 <- pcaComb$x %>%
  as.data.frame() %>%
  rownames_to_column("sample") %>%
  as_tibble() %>%
  dplyr::select(sample, PC1, PC2) %>%
  left_join(rownames_to_column(dgeFiltComb$samples, "sample")) %>%
  mutate(
    data = case_when(
      str_detect(.$sample, "_") == TRUE ~ "2017",
      str_detect(.$sample, "_") == FALSE ~ "2019"
    )
  ) %>%
  ggplot(aes(PC1, PC2, colour = data, label = sample, shape = group)) +
  geom_point() +
  geom_text_repel(show.legend = FALSE) +
  theme_bw() +
  theme(legend.position = c(1.2, 0.5))
pca13 <- pcaComb$x %>%
  as.data.frame() %>%
  rownames_to_column("sample") %>%
  as_tibble() %>%
  dplyr::select(sample, PC1, PC3) %>%
  left_join(rownames_to_column(dgeFiltComb$samples, "sample")) %>%
  mutate(
    data = case_when(
      str_detect(.$sample, "_") == TRUE ~ "2017",
      str_detect(.$sample, "_") == FALSE ~ "2019"
    )
  ) %>%
  ggplot(aes(PC1, PC3, colour = data, label = sample, shape = group)) +
  geom_point() +
  geom_text_repel(show.legend = FALSE) +
  theme_bw() +
  theme(legend.position = "none")
pca23 <- pcaComb$x %>%
  as.data.frame() %>%
  rownames_to_column("sample") %>%
  as_tibble() %>%
  dplyr::select(sample, PC2, PC3) %>%
  left_join(rownames_to_column(dgeFiltComb$samples, "sample")) %>%
  mutate(
    data = case_when(
      str_detect(.$sample, "_") == TRUE ~ "2017",
      str_detect(.$sample, "_") == FALSE ~ "2019"
    )
  ) %>%
  ggplot(aes(PC2, PC3, colour = data, label = sample, shape = group)) +
  geom_point() +
  geom_text_repel(show.legend = FALSE) +
  theme_bw() +
  theme(legend.position = "none")

# Set up grid for visualisation of 3 PCA's at once.
vp1 <- viewport(x = 0.25, y = 0.25, width = 0.5, height = 0.5)
vp2 <- viewport(x = 0.25, y = 0.75, width = 0.5, height = 0.5)
vp3 <- viewport(x = 0.75, y = 0.75, width = 0.5, height = 0.5)
# Plot PCA plots in grid
if (interactive()) grid::grid.newpage()
print(pca12, vp = vp1)
print(pca13, vp = vp2)
print(pca23, vp = vp3)

RUVSeq